On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/83023 |
Resumo: | Nowadays, transportation vehicles are equipped with intelligent sensors. Together, they form collaborative networks that broadcast real-time data about mobility patterns in urban areas. Online intelligent transportation systems for taxi dispatching, time-saving route finding or automatic vehicle location are already exploring such information in the taxi/buses transport industries. In this PhD spotlight paper, the authors present two ML applications focused on improving the operation of Public Transportation (PT) systems: 1) Bus Bunching (BB) Online Detection and 2) Taxi-Passenger Demand Prediction. By doing so, we intend to give a brief overview of the type of approaches applicable to these type of problems. Our frameworks are straightforward. By employing online learning frameworks we are able to use both historical and real-time data to update the inference models. The results are promising. |
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On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning ApproachInteligência artificial, Engenharia electrotécnica, electrónica e informáticaArtificial intelligence, Electrical engineering, Electronic engineering, Information engineeringNowadays, transportation vehicles are equipped with intelligent sensors. Together, they form collaborative networks that broadcast real-time data about mobility patterns in urban areas. Online intelligent transportation systems for taxi dispatching, time-saving route finding or automatic vehicle location are already exploring such information in the taxi/buses transport industries. In this PhD spotlight paper, the authors present two ML applications focused on improving the operation of Public Transportation (PT) systems: 1) Bus Bunching (BB) Online Detection and 2) Taxi-Passenger Demand Prediction. By doing so, we intend to give a brief overview of the type of approaches applicable to these type of problems. Our frameworks are straightforward. By employing online learning frameworks we are able to use both historical and real-time data to update the inference models. The results are promising.20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/83023engLuís Moreira MatiasJoão Mendes MoreiraJoão GamaMichel Ferreirainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:03:14Zoai:repositorio-aberto.up.pt:10216/83023Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:53:34.277638Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach |
title |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach |
spellingShingle |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach Luís Moreira Matias Inteligência artificial, Engenharia electrotécnica, electrónica e informática Artificial intelligence, Electrical engineering, Electronic engineering, Information engineering |
title_short |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach |
title_full |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach |
title_fullStr |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach |
title_full_unstemmed |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach |
title_sort |
On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach |
author |
Luís Moreira Matias |
author_facet |
Luís Moreira Matias João Mendes Moreira João Gama Michel Ferreira |
author_role |
author |
author2 |
João Mendes Moreira João Gama Michel Ferreira |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Luís Moreira Matias João Mendes Moreira João Gama Michel Ferreira |
dc.subject.por.fl_str_mv |
Inteligência artificial, Engenharia electrotécnica, electrónica e informática Artificial intelligence, Electrical engineering, Electronic engineering, Information engineering |
topic |
Inteligência artificial, Engenharia electrotécnica, electrónica e informática Artificial intelligence, Electrical engineering, Electronic engineering, Information engineering |
description |
Nowadays, transportation vehicles are equipped with intelligent sensors. Together, they form collaborative networks that broadcast real-time data about mobility patterns in urban areas. Online intelligent transportation systems for taxi dispatching, time-saving route finding or automatic vehicle location are already exploring such information in the taxi/buses transport industries. In this PhD spotlight paper, the authors present two ML applications focused on improving the operation of Public Transportation (PT) systems: 1) Bus Bunching (BB) Online Detection and 2) Taxi-Passenger Demand Prediction. By doing so, we intend to give a brief overview of the type of approaches applicable to these type of problems. Our frameworks are straightforward. By employing online learning frameworks we are able to use both historical and real-time data to update the inference models. The results are promising. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 2014-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/83023 |
url |
https://hdl.handle.net/10216/83023 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799135855514746881 |